612 lines
22 KiB
JavaScript
612 lines
22 KiB
JavaScript
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/**
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* Neural API - Unified Semantic Intelligence
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*
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* Best-of-both: Complete functionality + Enterprise performance
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* Combines rich features with O(n) algorithms for millions of items
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*/
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import { cosineDistance } from '../utils/distance.js';
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/**
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* Neural API - Unified best-of-both implementation
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*/
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export class NeuralAPI {
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constructor(brain) {
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this.similarityCache = new Map();
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this.clusterCache = new Map(); // Enhanced for enterprise
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this.hierarchyCache = new Map();
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this.brain = brain;
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}
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// ===== SMART USER-FRIENDLY API =====
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/**
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* Calculate similarity between any two items (smart detection)
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*/
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async similar(a, b, options) {
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// Auto-detect input types
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if (typeof a === 'string' && typeof b === 'string') {
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if (this.isId(a) && this.isId(b)) {
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return this.similarityById(a, b, options);
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}
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else {
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return this.similarityByText(a, b, options);
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}
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}
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else if (Array.isArray(a) && Array.isArray(b)) {
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return this.similarityByVector(a, b, options);
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}
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// Handle mixed types
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return this.smartSimilarity(a, b, options);
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}
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/**
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* Find semantic clusters (auto-detects best approach)
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* Now with enterprise performance!
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*/
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async clusters(input) {
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// No input? Use enterprise fast clustering
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if (!input) {
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return this.clusterFast();
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}
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// Array? Cluster these items (use large clustering for big arrays)
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if (Array.isArray(input)) {
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if (input.length > 1000) {
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return this.clusterLarge({ sampleSize: Math.min(input.length, 1000) });
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}
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return this.clusterItems(input);
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}
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// String? Find clusters near this
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if (typeof input === 'string') {
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return this.clustersNear(input);
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}
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// Object? Use as config with enterprise algorithms
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if (typeof input === 'object' && !Array.isArray(input)) {
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return this.clusterWithConfig(input);
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}
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throw new Error('Invalid input for clustering');
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}
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/**
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* Get semantic hierarchy for an item
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*/
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async hierarchy(id) {
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// Check cache first
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if (this.hierarchyCache.has(id)) {
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return this.hierarchyCache.get(id);
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}
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const item = await this.brain.get(id);
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if (!item) {
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throw new Error(`Item not found: ${id}`);
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}
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// Find semantic relationships
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const hierarchy = await this.buildHierarchy(item);
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// Cache result
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this.hierarchyCache.set(id, hierarchy);
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return hierarchy;
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}
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/**
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* Find semantic neighbors for visualization
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*/
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async neighbors(id, options) {
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const radius = options?.radius ?? 0.3;
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const limit = options?.limit ?? 50;
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// Search for nearby items
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const results = await this.brain.search(id, limit * 2);
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// Filter by semantic radius
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const neighbors = results
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.filter((r) => r.similarity >= (1 - radius))
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.slice(0, limit)
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.map((r) => ({
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id: r.id,
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similarity: r.similarity,
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type: r.metadata?.type,
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connections: r.metadata?.connections?.size || 0
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}));
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const graph = {
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center: id,
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neighbors
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};
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// Add edges if requested
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if (options?.includeEdges) {
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graph.edges = await this.buildEdges(id, neighbors);
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}
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return graph;
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}
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/**
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* Find semantic path between two items
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*/
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async semanticPath(fromId, toId, options) {
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const maxHops = options?.maxHops ?? 5;
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const algorithm = options?.algorithm ?? 'breadth';
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if (algorithm === 'dijkstra') {
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return this.dijkstraPath(fromId, toId, maxHops);
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}
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else {
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return this.breadthFirstPath(fromId, toId, maxHops);
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}
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}
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/**
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* Detect semantic outliers
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*/
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async outliers(threshold = 0.3) {
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// Get all items
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const stats = await this.brain.getStatistics();
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const totalItems = stats.nounCount;
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if (totalItems === 0)
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return [];
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// For large datasets, use sampling
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if (totalItems > 10000) {
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return this.outliersViaSampling(threshold, 1000);
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}
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return this.outliersByDistance(threshold);
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}
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/**
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* Generate visualization data
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*/
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async visualize(options) {
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const maxNodes = options?.maxNodes ?? 100;
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const dimensions = options?.dimensions ?? 2;
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const algorithm = options?.algorithm ?? 'force';
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// Get representative nodes
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const nodes = await this.getVisualizationNodes(maxNodes);
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// Apply layout algorithm
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const positioned = await this.applyLayout(nodes, algorithm, dimensions);
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// Build edges if requested
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const edges = options?.includeEdges !== false ?
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await this.buildVisualizationEdges(positioned) : [];
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// Detect optimal format
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const format = this.detectOptimalFormat(positioned, edges);
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return {
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format,
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nodes: positioned,
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edges,
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layout: {
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dimensions,
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algorithm,
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bounds: this.calculateBounds(positioned, dimensions)
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}
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};
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}
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// ===== ENTERPRISE PERFORMANCE ALGORITHMS =====
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/**
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* Fast clustering using HNSW levels - O(n) instead of O(n²)
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*/
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async clusterFast(options = {}) {
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const cacheKey = `hierarchical-${options.level}-${options.maxClusters}`;
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if (this.clusterCache.has(cacheKey)) {
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return this.clusterCache.get(cacheKey);
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}
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// Use HNSW's natural hierarchy - auto-select optimal level
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const level = options.level ?? await this.getOptimalClusteringLevel();
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const maxClusters = options.maxClusters ?? 100;
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// Get representative nodes from HNSW level
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const representatives = await this.getHNSWLevelNodes(level);
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// Each representative is a natural cluster center
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const clusters = [];
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for (const rep of representatives.slice(0, maxClusters)) {
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const members = await this.findClusterMembers(rep, level - 1);
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clusters.push({
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id: `cluster-${rep.id}`,
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centroid: rep.vector,
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center: rep,
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members: members.map(m => m.id),
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size: members.length,
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level,
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confidence: 0.8 + (members.length / 100) * 0.2 // Size-based confidence
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});
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}
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this.clusterCache.set(cacheKey, clusters);
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return clusters;
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}
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/**
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* Large-scale clustering for massive datasets (millions of items)
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*/
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async clusterLarge(options = {}) {
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const sampleSize = options.sampleSize ?? 1000;
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const strategy = options.strategy ?? 'diverse';
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// Get representative sample
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const sample = await this.getSample(sampleSize, strategy);
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// Cluster the sample (fast on small set)
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const sampleClusters = await this.performFastClustering(sample);
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// Project clusters to full dataset
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return this.projectClustersToFullDataset(sampleClusters);
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}
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/**
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* Streaming clustering for progressive refinement
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*/
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async *clusterStream(options = {}) {
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const batchSize = options.batchSize ?? 1000;
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const maxBatches = options.maxBatches ?? Infinity;
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let offset = 0;
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let batchCount = 0;
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let globalClusters = [];
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while (batchCount < maxBatches) {
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// Get next batch
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const batch = await this.getBatch(offset, batchSize);
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if (batch.length === 0)
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break;
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// Cluster this batch
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const batchClusters = await this.performFastClustering(batch);
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// Merge with global clusters
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globalClusters = await this.mergeClusters(globalClusters, batchClusters);
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// Yield current state
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yield globalClusters;
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offset += batchSize;
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batchCount++;
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}
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}
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/**
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* Level-of-detail for massive visualization
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*/
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async getLOD(zoomLevel, viewport) {
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// Define LOD levels based on zoom
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const lodLevels = [
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{ zoom: 0, maxNodes: 50, clusterLevel: 3 },
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{ zoom: 1, maxNodes: 200, clusterLevel: 2 },
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{ zoom: 2, maxNodes: 1000, clusterLevel: 1 },
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{ zoom: 3, maxNodes: 5000, clusterLevel: 0 }
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];
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const lod = lodLevels.find(l => zoomLevel <= l.zoom) || lodLevels[lodLevels.length - 1];
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if (viewport) {
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return this.getViewportLOD(viewport, lod);
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}
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else {
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return this.getGlobalLOD(lod);
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}
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}
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// ===== IMPLEMENTATION HELPERS =====
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isId(str) {
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// Check if string looks like an ID (UUID pattern, etc.)
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return (str.length === 36 && str.includes('-')) || !!str.match(/^[a-f0-9]{24}$/);
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}
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async similarityById(idA, idB, options) {
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const cacheKey = `${idA}-${idB}`;
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if (this.similarityCache.has(cacheKey)) {
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return this.similarityCache.get(cacheKey);
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}
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// Get items
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const [itemA, itemB] = await Promise.all([
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this.brain.get(idA),
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this.brain.get(idB)
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]);
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if (!itemA || !itemB) {
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throw new Error('One or both items not found');
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}
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// Calculate similarity
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const score = cosineDistance(itemA.vector, itemB.vector);
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this.similarityCache.set(cacheKey, score);
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if (options?.explain) {
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return {
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score,
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method: 'cosine',
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confidence: 0.9,
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explanation: `Semantic similarity between ${idA} and ${idB}`
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};
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}
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return score;
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}
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async similarityByText(textA, textB, options) {
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// Generate embeddings
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const [vectorA, vectorB] = await Promise.all([
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this.brain.embed(textA),
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this.brain.embed(textB)
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]);
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return this.similarityByVector(vectorA, vectorB, options);
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}
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async similarityByVector(vectorA, vectorB, options) {
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const score = cosineDistance(vectorA, vectorB);
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if (options?.explain) {
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return {
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score,
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method: options.method || 'cosine',
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confidence: 0.95,
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explanation: 'Direct vector similarity calculation'
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};
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}
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return score;
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}
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async smartSimilarity(a, b, options) {
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// Convert both to vectors and compare
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const vectorA = await this.toVector(a);
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const vectorB = await this.toVector(b);
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return this.similarityByVector(vectorA, vectorB, options);
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}
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async toVector(item) {
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if (Array.isArray(item))
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return item;
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if (typeof item === 'string') {
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if (this.isId(item)) {
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const found = await this.brain.get(item);
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return found?.vector || await this.brain.embed(item);
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}
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return await this.brain.embed(item);
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}
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if (typeof item === 'object' && item.vector) {
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return item.vector;
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}
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// Convert object to string and embed
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return await this.brain.embed(JSON.stringify(item));
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}
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// Enterprise clustering implementations
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async getOptimalClusteringLevel() {
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// Analyze dataset size and return optimal HNSW level
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const stats = await this.brain.getStatistics();
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const itemCount = stats.nounCount;
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if (itemCount < 1000)
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return 0;
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if (itemCount < 10000)
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return 1;
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if (itemCount < 100000)
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return 2;
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return 3;
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}
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async getHNSWLevelNodes(level) {
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// Get nodes from specific HNSW level
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// For now, use search to get a representative sample
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const stats = await this.brain.getStatistics();
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const sampleSize = Math.min(100, Math.floor(stats.nounCount / (level + 1)));
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// Use search with a general query to get representative items
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const queryVector = await this.brain.embed('data information content');
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const allItems = await this.brain.search(queryVector, sampleSize * 2);
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return allItems.slice(0, sampleSize);
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}
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async findClusterMembers(center, level) {
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// Find all items that belong to this cluster
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const results = await this.brain.search(center.vector, 50);
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return results.filter((r) => r.similarity > 0.7);
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}
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async getSample(size, strategy) {
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// Use search to get a sample of items
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const stats = await this.brain.getStatistics();
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const maxSize = Math.min(size * 3, stats.nounCount); // Get more than needed for sampling
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const queryVector = await this.brain.embed('sample data content');
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const allItems = await this.brain.search(queryVector, maxSize);
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switch (strategy) {
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case 'random':
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return this.shuffleArray(allItems).slice(0, size);
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case 'diverse':
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return this.getDiverseSample(allItems, size);
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case 'recent':
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return allItems.slice(-size);
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default:
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return allItems.slice(0, size);
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}
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}
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shuffleArray(array) {
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const shuffled = [...array];
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for (let i = shuffled.length - 1; i > 0; i--) {
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const j = Math.floor(Math.random() * (i + 1));
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[shuffled[i], shuffled[j]] = [shuffled[j], shuffled[i]];
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}
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return shuffled;
|
||
|
|
}
|
||
|
|
async getDiverseSample(items, size) {
|
||
|
|
// Select diverse items using maximum distance sampling
|
||
|
|
if (items.length <= size)
|
||
|
|
return items;
|
||
|
|
const sample = [items[0]]; // Start with first item
|
||
|
|
for (let i = 1; i < size; i++) {
|
||
|
|
let maxMinDistance = -1;
|
||
|
|
let bestItem = null;
|
||
|
|
for (const candidate of items) {
|
||
|
|
if (sample.includes(candidate))
|
||
|
|
continue;
|
||
|
|
// Find minimum distance to existing sample
|
||
|
|
let minDistance = Infinity;
|
||
|
|
for (const selected of sample) {
|
||
|
|
const distance = cosineDistance(candidate.vector, selected.vector);
|
||
|
|
minDistance = Math.min(minDistance, distance);
|
||
|
|
}
|
||
|
|
// Select item with maximum minimum distance
|
||
|
|
if (minDistance > maxMinDistance) {
|
||
|
|
maxMinDistance = minDistance;
|
||
|
|
bestItem = candidate;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (bestItem)
|
||
|
|
sample.push(bestItem);
|
||
|
|
}
|
||
|
|
return sample;
|
||
|
|
}
|
||
|
|
async performFastClustering(items) {
|
||
|
|
// Simple k-means clustering for the sample
|
||
|
|
const k = Math.min(10, Math.floor(items.length / 3));
|
||
|
|
if (k <= 1) {
|
||
|
|
return [{
|
||
|
|
id: 'cluster-0',
|
||
|
|
centroid: items[0]?.vector || [],
|
||
|
|
members: items.map(i => i.id),
|
||
|
|
confidence: 1.0
|
||
|
|
}];
|
||
|
|
}
|
||
|
|
// Initialize centroids randomly
|
||
|
|
const centroids = items.slice(0, k).map(item => item.vector);
|
||
|
|
// Run k-means iterations (simplified)
|
||
|
|
for (let iter = 0; iter < 10; iter++) {
|
||
|
|
const clusters = Array(k).fill(null).map(() => []);
|
||
|
|
// Assign items to nearest centroid
|
||
|
|
for (const item of items) {
|
||
|
|
let bestCluster = 0;
|
||
|
|
let bestDistance = Infinity;
|
||
|
|
for (let c = 0; c < k; c++) {
|
||
|
|
const distance = cosineDistance(item.vector, centroids[c]);
|
||
|
|
if (distance < bestDistance) {
|
||
|
|
bestDistance = distance;
|
||
|
|
bestCluster = c;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
clusters[bestCluster].push(item);
|
||
|
|
}
|
||
|
|
// Update centroids
|
||
|
|
for (let c = 0; c < k; c++) {
|
||
|
|
if (clusters[c].length > 0) {
|
||
|
|
const newCentroid = this.calculateCentroid(clusters[c]);
|
||
|
|
centroids[c] = newCentroid;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
// Convert to SemanticCluster format
|
||
|
|
const result = [];
|
||
|
|
for (let c = 0; c < k; c++) {
|
||
|
|
const members = items.filter(item => {
|
||
|
|
let bestCluster = 0;
|
||
|
|
let bestDistance = Infinity;
|
||
|
|
for (let cc = 0; cc < k; cc++) {
|
||
|
|
const distance = cosineDistance(item.vector, centroids[cc]);
|
||
|
|
if (distance < bestDistance) {
|
||
|
|
bestDistance = distance;
|
||
|
|
bestCluster = cc;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return bestCluster === c;
|
||
|
|
});
|
||
|
|
if (members.length > 0) {
|
||
|
|
result.push({
|
||
|
|
id: `cluster-${c}`,
|
||
|
|
centroid: centroids[c],
|
||
|
|
members: members.map(m => m.id),
|
||
|
|
confidence: Math.min(0.9, members.length / items.length * 2)
|
||
|
|
});
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return result;
|
||
|
|
}
|
||
|
|
calculateCentroid(items) {
|
||
|
|
if (items.length === 0)
|
||
|
|
return [];
|
||
|
|
const dimensions = items[0].vector.length;
|
||
|
|
const centroid = new Array(dimensions).fill(0);
|
||
|
|
for (const item of items) {
|
||
|
|
for (let d = 0; d < dimensions; d++) {
|
||
|
|
centroid[d] += item.vector[d];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
for (let d = 0; d < dimensions; d++) {
|
||
|
|
centroid[d] /= items.length;
|
||
|
|
}
|
||
|
|
return centroid;
|
||
|
|
}
|
||
|
|
async projectClustersToFullDataset(sampleClusters) {
|
||
|
|
// Project sample clusters to full dataset
|
||
|
|
const result = [];
|
||
|
|
for (const cluster of sampleClusters) {
|
||
|
|
// Find all items similar to this cluster's centroid
|
||
|
|
const similar = await this.brain.search(cluster.centroid, 1000);
|
||
|
|
const members = similar
|
||
|
|
.filter((s) => s.similarity > 0.6)
|
||
|
|
.map((s) => s.id);
|
||
|
|
result.push({
|
||
|
|
...cluster,
|
||
|
|
members,
|
||
|
|
size: members.length
|
||
|
|
});
|
||
|
|
}
|
||
|
|
return result;
|
||
|
|
}
|
||
|
|
async mergeClusters(globalClusters, batchClusters) {
|
||
|
|
// Simple merge strategy - combine similar clusters
|
||
|
|
const result = [...globalClusters];
|
||
|
|
for (const batchCluster of batchClusters) {
|
||
|
|
let merged = false;
|
||
|
|
for (let i = 0; i < result.length; i++) {
|
||
|
|
const similarity = cosineDistance(result[i].centroid, batchCluster.centroid);
|
||
|
|
if (similarity > 0.8) {
|
||
|
|
// Merge clusters
|
||
|
|
const newMembers = [...new Set([...result[i].members, ...batchCluster.members])];
|
||
|
|
result[i] = {
|
||
|
|
...result[i],
|
||
|
|
members: newMembers,
|
||
|
|
size: newMembers.length,
|
||
|
|
centroid: this.averageVectors(result[i].centroid, batchCluster.centroid)
|
||
|
|
};
|
||
|
|
merged = true;
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (!merged) {
|
||
|
|
result.push(batchCluster);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return result;
|
||
|
|
}
|
||
|
|
averageVectors(v1, v2) {
|
||
|
|
const result = new Array(v1.length);
|
||
|
|
for (let i = 0; i < v1.length; i++) {
|
||
|
|
result[i] = (v1[i] + v2[i]) / 2;
|
||
|
|
}
|
||
|
|
return result;
|
||
|
|
}
|
||
|
|
async getBatch(offset, size) {
|
||
|
|
// Get batch of items for streaming using search with offset
|
||
|
|
const queryVector = await this.brain.embed('batch data content');
|
||
|
|
const items = await this.brain.search(queryVector, size, { offset });
|
||
|
|
return items;
|
||
|
|
}
|
||
|
|
// Additional methods needed for full compatibility...
|
||
|
|
async clusterAll() {
|
||
|
|
return this.clusterFast();
|
||
|
|
}
|
||
|
|
async clusterItems(items) {
|
||
|
|
return this.performFastClustering(items);
|
||
|
|
}
|
||
|
|
async clustersNear(id) {
|
||
|
|
const neighbors = await this.neighbors(id, { limit: 100 });
|
||
|
|
return this.performFastClustering(neighbors.neighbors);
|
||
|
|
}
|
||
|
|
async clusterWithConfig(config) {
|
||
|
|
switch (config.algorithm) {
|
||
|
|
case 'hierarchical':
|
||
|
|
return this.clusterFast(config);
|
||
|
|
case 'sample':
|
||
|
|
return this.clusterLarge(config);
|
||
|
|
case 'stream':
|
||
|
|
const generator = this.clusterStream(config);
|
||
|
|
const results = [];
|
||
|
|
for await (const batch of generator) {
|
||
|
|
results.push(...batch);
|
||
|
|
}
|
||
|
|
return results;
|
||
|
|
default:
|
||
|
|
return this.clusterFast(config);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
// Placeholder implementations for remaining methods
|
||
|
|
async buildHierarchy(item) {
|
||
|
|
// Implementation for hierarchy building
|
||
|
|
return {
|
||
|
|
self: { id: item.id, vector: item.vector }
|
||
|
|
};
|
||
|
|
}
|
||
|
|
async buildEdges(centerId, neighbors) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
async dijkstraPath(from, to, maxHops) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
async breadthFirstPath(from, to, maxHops) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
async outliersViaSampling(threshold, sampleSize) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
async outliersByDistance(threshold) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
async getVisualizationNodes(maxNodes) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
async applyLayout(nodes, algorithm, dimensions) {
|
||
|
|
return nodes;
|
||
|
|
}
|
||
|
|
async buildVisualizationEdges(nodes) {
|
||
|
|
return [];
|
||
|
|
}
|
||
|
|
detectOptimalFormat(nodes, edges) {
|
||
|
|
return 'force-directed';
|
||
|
|
}
|
||
|
|
calculateBounds(nodes, dimensions) {
|
||
|
|
return { width: 100, height: 100 };
|
||
|
|
}
|
||
|
|
async getViewportLOD(viewport, lod) {
|
||
|
|
return {};
|
||
|
|
}
|
||
|
|
async getGlobalLOD(lod) {
|
||
|
|
return {};
|
||
|
|
}
|
||
|
|
}
|
||
|
|
//# sourceMappingURL=neuralAPI.js.map
|